{
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   "source": [
    "import gensim\n",
    "from gensim.models.doc2vec import Doc2Vec\n",
    "TaggededDocument = gensim.models.doc2vec.TaggedDocument\n",
    "def get_data():\n",
    "    with open(\"C:/Users/Administrator/Desktop/train.txt\", 'r', encoding = 'utf-8') as f:\n",
    "        docs = f.readlines()\n",
    "    train_data = []\n",
    "    for i, text in enumerate(docs):\n",
    "        word_list = text.split('')\n",
    "        word_list[len(word_list)-1] = word_list[len(word_list)-1].strip()\n",
    "        document = TaggededDocument(word_list, tags = [i])\n",
    "        train_data.append(document)\n",
    "    return trian_data\n",
    "def train_model(x_train):\n",
    "    model_dm = Doc2Vec(x_trian, min_count = 1, window = 3,vector_size = 20, negative = 5, workers = 4, dm = 1)\n",
    "    model_dm.train(x_train, total_examples = model_dm.corpus_count,epochs = 70)\n",
    "    model_dm.save(\"data/model_doc2vec\")\n",
    "    return model_dm\n",
    "def test():\n",
    "    model_dm = Doc2Vec.load(\"data/model_doc2vec\")\n",
    "    test_text = ['科学', '教育', '是', '难搞', '的']\n",
    "    inferred_vector_dm = model_dm.infer_vector(test_text)\n",
    "    sims = model_dm.docvecs.most_similar([inferred_vector_dm],topn= 10)\n",
    "    return sims\n",
    "if __name__ == ' __main__':\n",
    "    train_data = get_data()\n",
    "    model_dm = train_model(train_data)\n",
    "    sims = test()\n",
    "    print(\"相似文本、相似度和文本中词的数量: \")\n",
    "    for count, sim in sims:\n",
    "        sentence = train_data[count]\n",
    "        words = ''\n",
    "        for word in sentence[0]:\n",
    "            words = words + word + ''\n",
    "        print(words, sim, len(sentence[0]))"
   ]
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